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Unverified Commit 292140b9 authored by Thomas Wolf's avatar Thomas Wolf Committed by GitHub
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Merge pull request #781 from huggingface/embeddings 

Clean up input embeddings resizing and weights tying
parents 3821ecbf c57e9d94
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Showing with 925 additions and 911 deletions
pytorch_transformers/modeling_bert.py
View file @ 292140b9
......@@ -507,23 +507,17 @@ class BertPredictionHeadTransform(nn.Module):
class BertLMPredictionHead(nn.Module):
def __init__(self, config, bert_model_embedding_weights):
def __init__(self, config):
super(BertLMPredictionHead, self).__init__()
self.transform = BertPredictionHeadTransform(config)
self.torchscript = config.torchscript
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
self.decoder = nn.Linear(bert_model_embedding_weights.size(1),
bert_model_embedding_weights.size(0),
self.decoder = nn.Linear(config.hidden_size,
config.vocab_size,
bias=False)
if self.torchscript:
self.decoder.weight = nn.Parameter(bert_model_embedding_weights.clone())
else:
self.decoder.weight = bert_model_embedding_weights
self.bias = nn.Parameter(torch.zeros(bert_model_embedding_weights.size(0)))
self.bias = nn.Parameter(torch.zeros(config.vocab_size))
def forward(self, hidden_states):
hidden_states = self.transform(hidden_states)
......@@ -532,9 +526,9 @@ class BertLMPredictionHead(nn.Module):
class BertOnlyMLMHead(nn.Module):
def __init__(self, config, bert_model_embedding_weights):
def __init__(self, config):
super(BertOnlyMLMHead, self).__init__()
self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
self.predictions = BertLMPredictionHead(config)
def forward(self, sequence_output):
prediction_scores = self.predictions(sequence_output)
......@@ -552,9 +546,9 @@ class BertOnlyNSPHead(nn.Module):
class BertPreTrainingHeads(nn.Module):
def __init__(self, config, bert_model_embedding_weights):
def __init__(self, config):
super(BertPreTrainingHeads, self).__init__()
self.predictions = BertLMPredictionHead(config, bert_model_embedding_weights)
self.predictions = BertLMPredictionHead(config)
self.seq_relationship = nn.Linear(config.hidden_size, 2)
def forward(self, sequence_output, pooled_output):
......@@ -619,6 +613,12 @@ class BertModel(BertPreTrainedModel):
self.apply(self.init_weights)
def _resize_token_embeddings(self, new_num_tokens):
old_embeddings = self.embeddings.word_embeddings
new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens)
self.embeddings.word_embeddings = new_embeddings
return self.embeddings.word_embeddings
def _prune_heads(self, heads_to_prune):
""" Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
......@@ -750,9 +750,17 @@ class BertForPreTraining(BertPreTrainedModel):
super(BertForPreTraining, self).__init__(config)
self.bert = BertModel(config)
self.cls = BertPreTrainingHeads(config, self.bert.embeddings.word_embeddings.weight)
self.cls = BertPreTrainingHeads(config)
self.apply(self.init_weights)
self.tie_weights()
def tie_weights(self):
""" Make sure we are sharing the input and output embeddings.
Export to TorchScript can't handle parameter sharing so we are cloning them instead.
"""
self._tie_or_clone_weights(self.cls.predictions.decoder,
self.bert.embeddings.word_embeddings)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None,
next_sentence_label=None, head_mask=None):
......@@ -845,9 +853,17 @@ class BertForMaskedLM(BertPreTrainedModel):
super(BertForMaskedLM, self).__init__(config)
self.bert = BertModel(config)
self.cls = BertOnlyMLMHead(config, self.bert.embeddings.word_embeddings.weight)
self.cls = BertOnlyMLMHead(config)
self.apply(self.init_weights)
self.tie_weights()
def tie_weights(self):
""" Make sure we are sharing the input and output embeddings.
Export to TorchScript can't handle parameter sharing so we are cloning them instead.
"""
self._tie_or_clone_weights(self.cls.predictions.decoder,
self.bert.embeddings.word_embeddings)
def forward(self, input_ids, token_type_ids=None, attention_mask=None, masked_lm_labels=None, head_mask=None):
"""
......
pytorch_transformers/modeling_gpt2.py
View file @ 292140b9
......@@ -104,7 +104,6 @@ class GPT2Config(PretrainedConfig):
Args:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `GPT2Model` or a configuration json file.
n_special: The number of special tokens to learn during fine-tuning ('[SEP]', '[CLF]', ...)
n_positions: Number of positional embeddings.
n_ctx: Size of the causal mask (usually same as n_positions).
n_embd: Dimensionality of the embeddings and hidden states.
......@@ -119,14 +118,12 @@ class GPT2Config(PretrainedConfig):
embd_pdrop: The dropout ratio for the embeddings.
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
predict_special_tokens: should we predict special tokens (when the model has a LM head)
"""
pretrained_config_archive_map = GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(
self,
vocab_size_or_config_json_file=50257,
n_special=0,
n_positions=1024,
n_ctx=1024,
n_embd=768,
......@@ -137,7 +134,6 @@ class GPT2Config(PretrainedConfig):
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
predict_special_tokens=True,
num_labels=1,
summary_type='token_ids',
......@@ -151,7 +147,6 @@ class GPT2Config(PretrainedConfig):
Args:
vocab_size_or_config_json_file: Vocabulary size of `inputs_ids` in `GPT2Model` or a configuration json file.
n_special: The number of special tokens to learn during fine-tuning ('[SEP]', '[CLF]', ...)
n_positions: Number of positional embeddings.
n_ctx: Size of the causal mask (usually same as n_positions).
n_embd: Dimensionality of the embeddings and hidden states.
......@@ -166,7 +161,6 @@ class GPT2Config(PretrainedConfig):
embd_pdrop: The dropout ratio for the embeddings.
initializer_range: The sttdev of the truncated_normal_initializer for
initializing all weight matrices.
predict_special_tokens: should we predict special tokens (when the model has a LM head)
"""
super(GPT2Config, self).__init__(**kwargs)
......@@ -178,7 +172,6 @@ class GPT2Config(PretrainedConfig):
self.__dict__[key] = value
elif isinstance(vocab_size_or_config_json_file, int):
self.vocab_size = vocab_size_or_config_json_file
self.n_special = n_special
self.n_ctx = n_ctx
self.n_positions = n_positions
self.n_embd = n_embd
......@@ -189,7 +182,6 @@ class GPT2Config(PretrainedConfig):
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.predict_special_tokens = predict_special_tokens
self.num_labels = num_labels
self.summary_type = summary_type
......@@ -203,10 +195,6 @@ class GPT2Config(PretrainedConfig):
"or the path to a pretrained model config file (str)"
)
@property
def total_tokens_embeddings(self):
return self.vocab_size + self.n_special
@property
def hidden_size(self):
return self.n_embd
......@@ -347,34 +335,6 @@ class Block(nn.Module):
return outputs # x, present, (attentions)
class GPT2LMHead(nn.Module):
""" Language Model Head for the transformer """
def __init__(self, model_embeddings_weights, config):
super(GPT2LMHead, self).__init__()
self.n_embd = config.n_embd
self.vocab_size = config.vocab_size
self.predict_special_tokens = config.predict_special_tokens
self.torchscript = config.torchscript
embed_shape = model_embeddings_weights.shape
self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
self.set_embeddings_weights(model_embeddings_weights)
def set_embeddings_weights(self, model_embeddings_weights, predict_special_tokens=True):
self.predict_special_tokens = predict_special_tokens
# Export to TorchScript can't handle parameter sharing so we are cloning them.
if self.torchscript:
self.decoder.weight = nn.Parameter(model_embeddings_weights.clone())
else:
self.decoder.weight = model_embeddings_weights # Tied weights
def forward(self, hidden_state):
lm_logits = self.decoder(hidden_state)
if not self.predict_special_tokens:
lm_logits = lm_logits[..., :self.vocab_size]
return lm_logits
class GPT2PreTrainedModel(PreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
......@@ -400,36 +360,6 @@ class GPT2PreTrainedModel(PreTrainedModel):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
"""
Instantiate a GPT2PreTrainedModel from a pre-trained model file or a pytorch state dict.
Download and cache the pre-trained model file if needed.
Params:
pretrained_model_name_or_path: either:
- a str with the name of a pre-trained model to load selected in the list of:
. `gpt2`
- a path or url to a pretrained model archive containing:
. `gpt2_config.json` a configuration file for the model
. `pytorch_model.bin` a PyTorch dump of a GPT2Model instance
- a path or url to a pretrained model archive containing:
. `gpt2_config.json` a configuration file for the model
. a TensorFlow checkpoint with trained weights
from_tf: should we load the weights from a locally saved TensorFlow checkpoint
cache_dir: an optional path to a folder in which the pre-trained models will be cached.
state_dict: an optional state dictionary (collections.OrderedDict object) to use instead of pre-trained models
*inputs, **kwargs: additional input for the specific GPT2 class
"""
num_special_tokens = kwargs.pop('num_special_tokens', None)
model = super(GPT2PreTrainedModel, cls).from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
# Add additional embeddings for special tokens if needed
# This step also make sure we are still sharing the output and input embeddings after loading weights
model.set_num_special_tokens(num_special_tokens)
return model
class GPT2Model(GPT2PreTrainedModel):
"""OpenAI GPT-2 model ("Language Models are Unsupervised Multitask Learners").
......@@ -447,13 +377,13 @@ class GPT2Model(GPT2PreTrainedModel):
config.vocab_size - 1, ______________________
config.vocab_size,
... -> special embeddings
config.vocab_size + config.n_special - 1] ______________________
config.vocab_size + n_special - 1] ______________________
where total_tokens_embeddings can be obtained as config.total_tokens_embeddings and is equal to
where total_tokens_embeddings is equal to
::
total_tokens_embeddings = config.vocab_size + config.n_special
total_tokens_embeddings = vocab_size + n_special
You should use the associated indices to index the embeddings.
......@@ -474,7 +404,7 @@ class GPT2Model(GPT2PreTrainedModel):
self.output_hidden_states = config.output_hidden_states
self.output_attentions = config.output_attentions
self.wte = nn.Embedding(config.total_tokens_embeddings, config.n_embd)
self.wte = nn.Embedding(config.vocab_size, config.n_embd)
self.wpe = nn.Embedding(config.n_positions, config.n_embd)
self.drop = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList([Block(config.n_ctx, config, scale=True) for _ in range(config.n_layer)])
......@@ -482,26 +412,9 @@ class GPT2Model(GPT2PreTrainedModel):
self.apply(self.init_weights)
def set_num_special_tokens(self, num_special_tokens=None):
"""
Update input embeddings with new embedding matrix if needed.
Args:
num_special_tokens: Special tokens to be added to the embedding matrix
TODO Lysandre filled args
"""
if num_special_tokens is None or self.config.n_special == num_special_tokens:
return
# Update config
self.config.n_special = num_special_tokens
# Build new embeddings and initialize all new embeddings (in particular the special tokens)
old_embed = self.wte
self.wte = nn.Embedding(self.config.total_tokens_embeddings, self.config.n_embd)
self.wte.to(old_embed.weight.device)
self.init_weights(self.wte)
# Copy word embeddings from the previous weights
self.wte.weight.data[:self.config.vocab_size, :] = old_embed.weight.data[:self.config.vocab_size, :]
def _resize_token_embeddings(self, new_num_tokens):
self.wte = self._get_resized_embeddings(self.wte, new_num_tokens)
return self.wte
def _prune_heads(self, heads_to_prune):
""" Prunes heads of the model.
......@@ -641,23 +554,17 @@ class GPT2LMHeadModel(GPT2PreTrainedModel):
def __init__(self, config):
super(GPT2LMHeadModel, self).__init__(config)
self.transformer = GPT2Model(config)
self.lm_head = GPT2LMHead(self.transformer.wte.weight, config)
self.apply(self.init_weights)
def set_num_special_tokens(self, num_special_tokens, predict_special_tokens=True):
"""
Update input and output embeddings with new embedding matrix. Make sure we are sharing the embeddings.
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
Args:
num_special_tokens: Special tokens to be added to the embedding matrix
predict_special_tokens: if set to True, the model will try and predict the specified ``num_special_tokens``.
Defaults to True.
self.apply(self.init_weights)
self.tie_weights()
TODO Lysandre filled args
def tie_weights(self):
""" Make sure we are sharing the input and output embeddings.
Export to TorchScript can't handle parameter sharing so we are cloning them instead.
"""
self.config.predict_special_tokens = self.transformer.config.predict_special_tokens = predict_special_tokens
self.transformer.set_num_special_tokens(num_special_tokens)
self.lm_head.set_embeddings_weights(self.transformer.wte.weight, predict_special_tokens=predict_special_tokens)
self._tie_or_clone_weights(self.lm_head,
self.transformer.wte)
def forward(self, input_ids, position_ids=None, token_type_ids=None, lm_labels=None, past=None, head_mask=None):
"""
......@@ -740,25 +647,17 @@ class GPT2DoubleHeadsModel(GPT2PreTrainedModel):
def __init__(self, config):
super(GPT2DoubleHeadsModel, self).__init__(config)
self.transformer = GPT2Model(config)
self.lm_head = GPT2LMHead(self.transformer.wte.weight, config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.multiple_choice_head = SequenceSummary(config)
self.apply(self.init_weights)
def set_num_special_tokens(self, num_special_tokens, predict_special_tokens=True):
"""
Update input and output embeddings with new embedding matrix.Make sure we are sharing the embeddings
Args:
num_special_tokens: Special tokens to be added to the embedding matrix
predict_special_tokens: if set to True, the model will try and predict the specified ``num_special_tokens``.
Defaults to True.
TODO Lysandre filled args
def tie_weights(self):
""" Make sure we are sharing the input and output embeddings.
Export to TorchScript can't handle parameter sharing so we are cloning them instead.
"""
self.config.predict_special_tokens = self.transformer.config.predict_special_tokens = predict_special_tokens
self.transformer.set_num_special_tokens(num_special_tokens)
self.lm_head.set_embeddings_weights(self.transformer.wte.weight, predict_special_tokens=predict_special_tokens)
self._tie_or_clone_weights(self.lm_head,
self.transformer.wte)
def forward(self, input_ids, mc_token_ids=None, lm_labels=None, mc_labels=None, token_type_ids=None,
position_ids=None, past=None, head_mask=None):
......
pytorch_transformers/modeling_openai.py
View file @ 292140b9
......@@ -156,7 +156,6 @@ class OpenAIGPTConfig(PretrainedConfig):
def __init__(
self,
vocab_size_or_config_json_file=40478,
n_special=0,
n_positions=512,
n_ctx=512,
n_embd=768,
......@@ -190,7 +189,6 @@ class OpenAIGPTConfig(PretrainedConfig):
self.__dict__[key] = value
elif isinstance(vocab_size_or_config_json_file, int):
self.vocab_size = vocab_size_or_config_json_file
self.n_special = n_special
self.n_ctx = n_ctx
self.n_positions = n_positions
self.n_embd = n_embd
......@@ -216,10 +214,6 @@ class OpenAIGPTConfig(PretrainedConfig):
"or the path to a pretrained model config file (str)"
)
@property
def total_tokens_embeddings(self):
return self.vocab_size + self.n_special
@property
def hidden_size(self):
return self.n_embd
......@@ -355,34 +349,6 @@ class Block(nn.Module):
return outputs
class OpenAIGPTLMHead(nn.Module):
""" Language Model Head for the transformer """
def __init__(self, model_embeddings_weights, config):
super(OpenAIGPTLMHead, self).__init__()
self.n_embd = config.n_embd
self.vocab_size = config.vocab_size
self.predict_special_tokens = config.predict_special_tokens
self.torchscript = config.torchscript
embed_shape = model_embeddings_weights.shape
self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
self.set_embeddings_weights(model_embeddings_weights)
def set_embeddings_weights(self, model_embeddings_weights, predict_special_tokens=True):
self.predict_special_tokens = predict_special_tokens
if self.torchscript:
self.decoder.weight = nn.Parameter(model_embeddings_weights.clone())
else:
self.decoder.weight = model_embeddings_weights # Tied weights
def forward(self, hidden_state):
lm_logits = self.decoder(hidden_state)
if not self.predict_special_tokens:
lm_logits = lm_logits[..., :self.vocab_size]
return lm_logits
class OpenAIGPTPreTrainedModel(PreTrainedModel):
""" An abstract class to handle weights initialization and
a simple interface for dowloading and loading pretrained models.
......@@ -408,36 +374,6 @@ class OpenAIGPTPreTrainedModel(PreTrainedModel):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
"""
Instantiate a OpenAIGPTPreTrainedModel from a pre-trained model file or a pytorch state dict.
Download and cache the pre-trained model file if needed.
Params:
pretrained_model_name_or_path: either:
- a str with the name of a pre-trained model to load selected in the list of:
- a path or url to a pretrained model archive containing:
. `config.json` a configuration file for the model
. `pytorch_model.bin` a PyTorch dump of a OpenAIGPTModel instance
- a path or url to a pretrained model archive containing:
. `config.json` a configuration file for the model
. a series of NumPy files containing OpenAI TensorFlow trained weights
from_tf: should we load the weights from a locally saved TensorFlow checkpoint
cache_dir: an optional path to a folder in which the pre-trained models will be cached.
state_dict: an optional state dictionnary (collections.OrderedDict object) to use instead of pre-trained models
*inputs, **kwargs: additional input for the specific OpenAI-GPT class
"""
num_special_tokens = kwargs.get('num_special_tokens', None)
kwargs.pop('num_special_tokens', None)
model = super(OpenAIGPTPreTrainedModel, cls).from_pretrained(pretrained_model_name_or_path, pretrained_model_name_or_path, *inputs, **kwargs)
# Add additional embeddings for special tokens if needed
# This step also make sure we are still sharing the output and input embeddings after loading weights
model.set_num_special_tokens(num_special_tokens)
return model
class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
"""OpenAI GPT model ("Improving Language Understanding by Generative Pre-Training").
......@@ -457,13 +393,13 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
config.vocab_size - 1, ______________________
config.vocab_size,
... -> special embeddings
config.vocab_size + config.n_special - 1] ______________________
config.vocab_size + n_special - 1] ______________________
where ``total_tokens_embeddings`` can be obtained as ``config.total_tokens_embeddings`` and is:
where ``total_tokens_embeddings`` is:
::
total_tokens_embeddings = config.vocab_size + config.n_special
total_tokens_embeddings = config.vocab_size + n_special
You should use the associated indices to index the embeddings.
......@@ -485,34 +421,16 @@ class OpenAIGPTModel(OpenAIGPTPreTrainedModel):
self.output_attentions = config.output_attentions
self.output_hidden_states = config.output_hidden_states
self.tokens_embed = nn.Embedding(config.total_tokens_embeddings, config.n_embd)
self.tokens_embed = nn.Embedding(config.vocab_size, config.n_embd)
self.positions_embed = nn.Embedding(config.n_positions, config.n_embd)
self.drop = nn.Dropout(config.embd_pdrop)
self.h = nn.ModuleList([Block(config.n_ctx, config, scale=True) for _ in range(config.n_layer)])
self.apply(self.init_weights)
def set_num_special_tokens(self, num_special_tokens=None):
"""
Update input embeddings with new embedding matrice if needed
Args:
num_special_tokens: Special tokens to be added to the embedding matrix
TODO Lysandre filled Args
"""
if num_special_tokens is None or self.config.n_special == num_special_tokens:
return
# Update config
self.config.n_special = num_special_tokens
# Build new embeddings and initialize all new embeddings (in particular the special tokens)
old_embed = self.tokens_embed
self.tokens_embed = nn.Embedding(self.config.total_tokens_embeddings, self.config.n_embd)
self.tokens_embed.to(old_embed.weight.device)
self.init_weights(self.tokens_embed)
# Copy word embeddings from the previous weights
self.tokens_embed.weight.data[:self.config.vocab_size, :] = old_embed.weight.data[:self.config.vocab_size, :]
def _resize_token_embeddings(self, new_num_tokens):
self.tokens_embed = self._get_resized_embeddings(self.tokens_embed, new_num_tokens)
return self.tokens_embed
def _prune_heads(self, heads_to_prune):
""" Prunes heads of the model.
......@@ -657,24 +575,17 @@ class OpenAIGPTLMHeadModel(OpenAIGPTPreTrainedModel):
def __init__(self, config):
super(OpenAIGPTLMHeadModel, self).__init__(config)
self.transformer = OpenAIGPTModel(config)
self.lm_head = OpenAIGPTLMHead(self.transformer.tokens_embed.weight, config)
self.apply(self.init_weights)
def set_num_special_tokens(self, num_special_tokens, predict_special_tokens=True):
"""
Update input and output embeddings with new embedding matrix. Make sure we are sharing the embeddings
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
Args:
num_special_tokens: Special tokens to be added to the embedding matrix
predict_special_tokens: if set to True, the model will try and predict the specified ``num_special_tokens``.
Defaults to True.
TODO Lysandre filled Args
self.apply(self.init_weights)
self.tie_weights()
def tie_weights(self):
""" Make sure we are sharing the input and output embeddings.
Export to TorchScript can't handle parameter sharing so we are cloning them instead.
"""
self.config.predict_special_tokens = self.transformer.config.predict_special_tokens = predict_special_tokens
self.transformer.set_num_special_tokens(num_special_tokens)
self.lm_head.set_embeddings_weights(self.transformer.tokens_embed.weight, predict_special_tokens=predict_special_tokens)
self._tie_or_clone_weights(self.lm_head,
self.transformer.tokens_embed)
def forward(self, input_ids, position_ids=None, token_type_ids=None, lm_labels=None, head_mask=None):
"""
......@@ -747,13 +658,13 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
config.vocab_size - 1, ______________________
config.vocab_size,
... -> special embeddings
config.vocab_size + config.n_special - 1] ______________________
config.vocab_size + n_special - 1] ______________________
where ``total_tokens_embeddings`` can be obtained as ``config.total_tokens_embeddings`` and is:
where ``total_tokens_embeddings`` is:
::
total_tokens_embeddings = config.vocab_size + config.n_special
total_tokens_embeddings = config.vocab_size + .n_special
You should use the associate indices to index the embeddings.
......@@ -773,24 +684,18 @@ class OpenAIGPTDoubleHeadsModel(OpenAIGPTPreTrainedModel):
super(OpenAIGPTDoubleHeadsModel, self).__init__(config)
self.transformer = OpenAIGPTModel(config)
self.lm_head = OpenAIGPTLMHead(self.transformer.tokens_embed.weight, config)
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.multiple_choice_head = SequenceSummary(config)
self.apply(self.init_weights)
self.tie_weights()
def set_num_special_tokens(self, num_special_tokens, predict_special_tokens=True):
""" Update input and output embeddings with new embedding matrix. Make sure we are sharing the embeddings.
Args:
num_special_tokens: Special tokens to be added to the embedding matrix
predict_special_tokens: if set to True, the model will try and predict the specified ``num_special_tokens``.
Defaults to True.
TODO Lysandre filled Args
def tie_weights(self):
""" Make sure we are sharing the input and output embeddings.
Export to TorchScript can't handle parameter sharing so we are cloning them instead.
"""
self.config.predict_special_tokens = self.transformer.config.predict_special_tokens = predict_special_tokens
self.transformer.set_num_special_tokens(num_special_tokens)
self.lm_head.set_embeddings_weights(self.transformer.tokens_embed.weight, predict_special_tokens=predict_special_tokens)
self._tie_or_clone_weights(self.lm_head,
self.transformer.tokens_embed)
def forward(self, input_ids, mc_token_ids=None, lm_labels=None, mc_labels=None, token_type_ids=None,
position_ids=None, head_mask=None):
......
pytorch_transformers/modeling_transfo_xl.py
View file @ 292140b9
......@@ -287,6 +287,14 @@ class TransfoXLConfig(PretrainedConfig):
raise ValueError("First argument must be either a vocabulary size (int)"
"or the path to a pretrained model config file (str)")
@property
def vocab_size(self):
return self.n_token
@vocab_size.setter
def vocab_size(self, value):
self.n_token = value
@property
def hidden_size(self):
return self.d_model
......@@ -998,6 +1006,9 @@ class TransfoXLModel(TransfoXLPreTrainedModel):
self.apply(self.init_weights)
def _resize_token_embeddings(self, new_num_tokens):
return self.word_emb
def backward_compatible(self):
self.sample_softmax = -1
......@@ -1273,12 +1284,19 @@ class TransfoXLLMHeadModel(TransfoXLPreTrainedModel):
else:
if self.config.tie_weight:
for i in range(len(self.crit.out_layers)):
self.crit.out_layers[i].weight = self.transformer.word_emb.emb_layers[i].weight
self._tie_or_clone_weights(self.crit.out_layers[i],
self.transformer.word_emb.emb_layers[i])
if self.config.tie_projs:
for i, tie_proj in enumerate(self.config.tie_projs):
if tie_proj and self.config.div_val == 1 and self.config.d_model != self.config.d_embed:
if self.config.torchscript:
self.crit.out_projs[i] = nn.Parameter(self.transformer.word_emb.emb_projs[0].clone())
else:
self.crit.out_projs[i] = self.transformer.word_emb.emb_projs[0]
elif tie_proj and self.config.div_val != 1:
if self.config.torchscript:
self.crit.out_projs[i] = nn.Parameter(self.transformer.word_emb.emb_projs[i].clone())
else:
self.crit.out_projs[i] = self.transformer.word_emb.emb_projs[i]
def reset_length(self, tgt_len, ext_len, mem_len):
......
pytorch_transformers/modeling_utils.py
View file @ 292140b9
......@@ -151,6 +151,7 @@ class PreTrainedModel(nn.Module):
pretrained_model_archive_map = {}
load_tf_weights = lambda model, config, path: None
base_model_prefix = ""
input_embeddings = None
def __init__(self, config, *inputs, **kwargs):
super(PreTrainedModel, self).__init__()
......@@ -164,12 +165,79 @@ class PreTrainedModel(nn.Module):
# Save config in model
self.config = config
def _get_resized_embeddings(self, old_embeddings, new_num_tokens=None):
""" Build a resized Embedding Module from a provided token Embedding Module.
Increasing the size will add newly initialized vectors at the end
Reducing the size will remove vectors from the end
Args:
new_num_tokens: (Optional) New number of tokens in the embedding matrix.
Increasing the size will add newly initialized vectors at the end
Reducing the size will remove vectors from the end
If not provided or None: return the provided token Embedding Module.
Return:
Pointer to the resized Embedding Module or the old Embedding Module if new_num_tokens is None
"""
if new_num_tokens is None:
return old_embeddings
old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
if old_num_tokens == new_num_tokens:
return old_embeddings
# Build new embeddings
new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim)
new_embeddings.to(old_embeddings.weight.device)
# initialize all new embeddings (in particular added tokens)
self.init_weights(new_embeddings)
# Copy word embeddings from the previous weights
num_tokens_to_copy = min(old_num_tokens, new_num_tokens)
new_embeddings.weight.data[:num_tokens_to_copy, :] = old_embeddings.weight.data[:num_tokens_to_copy, :]
return new_embeddings
def _tie_or_clone_weights(self, first_module, second_module):
""" Tie or clone module weights depending of weither we are using TorchScript or not
"""
if self.config.torchscript:
first_module.weight = nn.Parameter(second_module.weight.clone())
else:
first_module.weight = second_module.weight
def resize_token_embeddings(self, new_num_tokens=None):
""" Resize input token embeddings matrix of the model if new_num_tokens != config.vocab_size.
Args:
new_num_tokens: (Optional) New number of tokens in the embedding matrix.
Increasing the size will add newly initialized vectors at the end
Reducing the size will remove vectors from the end
If not provided or None: does nothing.
Return:
Pointer to the input tokens Embedding Module of the model
"""
base_model = getattr(self, self.base_model_prefix, self) # get the base model if needed
model_embeds = base_model._resize_token_embeddings(new_num_tokens)
if new_num_tokens is None:
return model_embeds
# Update base model and current model config
self.config.vocab_size = new_num_tokens
base_model.vocab_size = new_num_tokens
# Tie weights again if needed
if hasattr(self, 'tie_weights'):
self.tie_weights()
return model_embeds
def prune_heads(self, heads_to_prune):
""" Prunes heads of the base model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
"""
model_to_prune = getattr(self, self.base_model_prefix, self) # get the base model if needed
model_to_prune._prune_heads(heads_to_prune)
base_model = getattr(self, self.base_model_prefix, self) # get the base model if needed
base_model._prune_heads(heads_to_prune)
def save_pretrained(self, save_directory):
""" Save a model with its configuration file to a directory, so that it
......
pytorch_transformers/modeling_xlm.py
View file @ 292140b9
......@@ -104,7 +104,6 @@ class XLMConfig(PretrainedConfig):
def __init__(self,
vocab_size_or_config_json_file=30145,
n_special=0,
emb_dim=2048,
n_layers=12,
n_heads=16,
......@@ -148,7 +147,6 @@ class XLMConfig(PretrainedConfig):
self.__dict__[key] = value
elif isinstance(vocab_size_or_config_json_file, int):
self.n_words = vocab_size_or_config_json_file
self.n_special = n_special
self.emb_dim = emb_dim
self.n_layers = n_layers
self.n_heads = n_heads
......@@ -183,8 +181,12 @@ class XLMConfig(PretrainedConfig):
"or the path to a pretrained model config file (str)")
@property
def total_tokens_embeddings(self):
return self.n_words + self.n_special
def vocab_size(self):
return self.n_words
@vocab_size.setter
def vocab_size(self, value):
self.n_words = value
@property
def hidden_size(self):
......@@ -479,6 +481,10 @@ class XLMModel(XLMPreTrainedModel):
self.apply(self.init_weights)
def _resize_token_embeddings(self, new_num_tokens):
self.embeddings = self._get_resized_embeddings(self.embeddings, new_num_tokens)
return self.embeddings
def _prune_heads(self, heads_to_prune):
""" Prunes heads of the model.
heads_to_prune: dict of {layer_num: list of heads to prune in this layer}
......@@ -718,8 +724,6 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
"""
def __init__(self, config):
super(XLMWithLMHeadModel, self).__init__(config)
self.torchscript = config.torchscript
self.transformer = XLMModel(config)
self.pred_layer = XLMPredLayer(config)
......@@ -729,10 +733,7 @@ class XLMWithLMHeadModel(XLMPreTrainedModel):
def tie_weights(self):
""" Make sure we are sharing the embeddings
"""
if self.torchscript:
self.pred_layer.proj.weight = nn.Parameter(self.transformer.embeddings.weight.clone())
else:
self.pred_layer.proj.weight = self.transformer.embeddings.weight
self._tie_or_clone_weights(self.pred_layer.proj, self.transformer.embeddings)
def forward(self, input_ids, lengths=None, positions=None, langs=None, token_type_ids=None,
attention_mask=None, cache=None, labels=None, head_mask=None):
......
pytorch_transformers/modeling_xlnet.py
View file @ 292140b9
......@@ -312,6 +312,14 @@ class XLNetConfig(PretrainedConfig):
raise ValueError("First argument must be either a vocabulary size (int)"
"or the path to a pretrained model config file (str)")
@property
def vocab_size(self):
return self.n_token
@vocab_size.setter
def vocab_size(self, value):
self.n_token = value
@property
def hidden_size(self):
return self.d_model
......@@ -654,9 +662,12 @@ class XLNetModel(XLNetPreTrainedModel):
self.apply(self.init_weights)
def _resize_token_embeddings(self, new_num_tokens):
self.word_embedding = self._get_resized_embeddings(self.word_embedding, new_num_tokens)
return self.word_embedding
def _prune_heads(self, heads_to_prune):
logger.info("Head pruning is not implemented for XLNet")
pass
raise NotImplementedError
def create_mask(self, qlen, mlen):
"""
......@@ -970,23 +981,17 @@ class XLNetLMHeadModel(XLNetPreTrainedModel):
super(XLNetLMHeadModel, self).__init__(config)
self.attn_type = config.attn_type
self.same_length = config.same_length
self.torchscript = config.torchscript
self.transformer = XLNetModel(config)
self.lm_loss = nn.Linear(config.d_model, config.n_token, bias=True)
# Tie weights
self.apply(self.init_weights)
self.tie_weights()
def tie_weights(self):
""" Make sure we are sharing the embeddings
"""
if self.torchscript:
self.lm_loss.weight = nn.Parameter(self.transformer.word_embedding.weight.clone())
else:
self.lm_loss.weight = self.transformer.word_embedding.weight
self._tie_or_clone_weights(self.lm_loss, self.transformer.word_embedding)
def forward(self, input_ids, token_type_ids=None, input_mask=None, attention_mask=None,
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
......
pytorch_transformers/tests/modeling_bert_test.py
View file @ 292140b9
......@@ -26,10 +26,15 @@ from pytorch_transformers import (BertConfig, BertModel, BertForMaskedLM,
BertForTokenClassification, BertForMultipleChoice)
from pytorch_transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
from .modeling_tests_commons import (create_and_check_commons, ConfigTester, ids_tensor)
from .modeling_common_test import (CommonTestCases, ConfigTester, ids_tensor)
class BertModelTest(unittest.TestCase):
class BertModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (BertModel, BertForMaskedLM, BertForNextSentencePrediction,
BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification,
BertForTokenClassification)
class BertModelTester(object):
def __init__(self,
......@@ -55,9 +60,6 @@ class BertModelTest(unittest.TestCase):
num_labels=3,
num_choices=4,
scope=None,
all_model_classes = (BertModel, BertForMaskedLM, BertForNextSentencePrediction,
BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification,
BertForTokenClassification),
):
self.parent = parent
self.batch_size = batch_size
......@@ -81,7 +83,6 @@ class BertModelTest(unittest.TestCase):
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.all_model_classes = all_model_classes
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
......@@ -253,52 +254,59 @@ class BertModelTest(unittest.TestCase):
self.check_loss_output(result)
def create_and_check_bert_commons(self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels):
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, input_mask,
sequence_labels, token_labels, choice_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
create_and_check_commons(self, config, inputs_dict)
return config, inputs_dict
def test_default(self):
self.run_tester(BertModelTest.BertModelTester(self))
def setUp(self):
self.model_tester = BertModelTest.BertModelTester(self)
self.config_tester = ConfigTester(self, config_class=BertConfig, hidden_size=37)
def test_config(self):
config_tester = ConfigTester(self, config_class=BertConfig, hidden_size=37)
config_tester.run_common_tests()
self.config_tester.run_common_tests()
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/pytorch_transformers_test/"
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = BertModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
def run_tester(self, tester):
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_bert_model(*config_and_inputs)
def test_bert_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_model(*config_and_inputs)
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_bert_for_masked_lm(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_masked_lm(*config_and_inputs)
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_bert_for_multiple_choice(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_multiple_choice(*config_and_inputs)
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_bert_for_next_sequence_prediction(*config_and_inputs)
def test_for_next_sequence_prediction(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_next_sequence_prediction(*config_and_inputs)
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_bert_for_pretraining(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_pretraining(*config_and_inputs)
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_bert_for_question_answering(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_question_answering(*config_and_inputs)
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_bert_for_sequence_classification(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_sequence_classification(*config_and_inputs)
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_bert_for_token_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_token_classification(*config_and_inputs)
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_bert_commons(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/pytorch_transformers_test/"
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = BertModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
pytorch_transformers/tests/modeling_common_test.py 0 → 100644
View file @ 292140b9
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import os
import shutil
import json
import random
import unittest
import logging
import torch
from pytorch_transformers import PretrainedConfig, PreTrainedModel
from pytorch_transformers.modeling_bert import BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP
from pytorch_transformers.modeling_gpt2 import GPT2LMHeadModel, GPT2Config, GPT2_PRETRAINED_MODEL_ARCHIVE_MAP
def _config_zero_init(config):
configs_no_init = copy.deepcopy(config)
for key in configs_no_init.__dict__.keys():
if '_range' in key or '_std' in key:
setattr(configs_no_init, key, 0.0)
return configs_no_init
class CommonTestCases:
class CommonModelTester(unittest.TestCase):
model_tester = None
all_model_classes = ()
test_torchscript = True
test_pruning = True
test_resize_embeddings = True
def test_initialization(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
configs_no_init = _config_zero_init(config)
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(param.data.mean().item(), [0.0, 1.0],
msg="Parameter {} of model {} seems not properly initialized".format(name, model_class))
def test_attention_outputs(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config)
model.eval()
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, False)
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads,
self.model_tester.seq_length,
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
out_len = len(outputs)
# Check attention is always last and order is fine
config.output_attentions = True
config.output_hidden_states = True
model = model_class(config)
model.eval()
outputs = model(**inputs_dict)
self.assertEqual(out_len+1, len(outputs))
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)
attentions = outputs[-1]
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
self.assertListEqual(
list(attentions[0].shape[-3:]),
[self.model_tester.num_attention_heads,
self.model_tester.seq_length,
self.model_tester.key_len if hasattr(self.model_tester, 'key_len') else self.model_tester.seq_length])
def test_torchscript(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
self._create_and_check_torchscript(config, inputs_dict)
def test_torchscript_output_attentions(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_attentions = True
self._create_and_check_torchscript(config, inputs_dict)
def test_torchscript_output_hidden_state(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_hidden_states = True
self._create_and_check_torchscript(config, inputs_dict)
def _create_and_check_torchscript(self, config, inputs_dict):
if not self.test_torchscript:
return
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.torchscript = True
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.eval()
inputs = inputs_dict['input_ids'] # Let's keep only input_ids
try:
torch.jit.trace(model, inputs)
except RuntimeError:
self.fail("Couldn't trace module.")
try:
traced_gpt2 = torch.jit.trace(model, inputs)
torch.jit.save(traced_gpt2, "traced_model.pt")
except RuntimeError:
self.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load("traced_model.pt")
os.remove("traced_model.pt")
except ValueError:
self.fail("Couldn't load module.")
model.eval()
loaded_model.eval()
model_params = model.parameters()
loaded_model_params = loaded_model.parameters()
models_equal = True
for p1, p2 in zip(model_params, loaded_model_params):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_headmasking(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
config.output_attentions = True
config.output_hidden_states = True
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
for model_class in self.all_model_classes:
model = model_class(config=configs_no_init)
model.eval()
# Prepare head_mask
# Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
head_mask = torch.ones(self.model_tester.num_hidden_layers, self.model_tester.num_attention_heads)
head_mask[0, 0] = 0
head_mask[-1, :-1] = 0
head_mask.requires_grad_(requires_grad=True)
inputs = inputs_dict.copy()
inputs['head_mask'] = head_mask
outputs = model(**inputs)
# Test that we can get a gradient back for importance score computation
output = sum(t.sum() for t in outputs[0])
output = output.sum()
output.backward()
multihead_outputs = head_mask.grad
attentions = outputs[-1]
hidden_states = outputs[-2]
# Remove Nan
self.assertIsNotNone(multihead_outputs)
self.assertEqual(len(multihead_outputs), self.model_tester.num_hidden_layers)
self.assertAlmostEqual(
attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(
attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(
attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
self.assertAlmostEqual(
attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
self.assertNotEqual(
attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
def test_head_pruning(self):
if not self.test_pruning:
return
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config=config)
model.eval()
heads_to_prune = {0: list(range(1, self.model_tester.num_attention_heads)),
-1: [0]}
model.prune_heads(heads_to_prune)
outputs = model(**inputs_dict)
attentions = outputs[-1]
self.assertEqual(
attentions[0].shape[-3], 1)
self.assertEqual(
attentions[1].shape[-3], self.model_tester.num_attention_heads)
self.assertEqual(
attentions[-1].shape[-3], self.model_tester.num_attention_heads - 1)
def test_hidden_states_output(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
config.output_hidden_states = True
config.output_attentions = False
model = model_class(config)
model.eval()
outputs = model(**inputs_dict)
hidden_states = outputs[-1]
self.assertEqual(model.config.output_attentions, False)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(len(hidden_states), self.model_tester.num_hidden_layers + 1)
self.assertListEqual(
list(hidden_states[0].shape[-2:]),
[self.model_tester.seq_length, self.model_tester.hidden_size])
def test_resize_tokens_embeddings(self):
original_config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.test_resize_embeddings:
return
for model_class in self.all_model_classes:
config = copy.deepcopy(original_config)
model = model_class(config)
model_vocab_size = config.vocab_size
# Retrieve the embeddings and clone theme
model_embed = model.resize_token_embeddings(model_vocab_size)
cloned_embeddings = model_embed.weight.clone()
# Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size + 10)
self.assertEqual(model.config.vocab_size, model_vocab_size + 10)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
# Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
model_embed = model.resize_token_embeddings(model_vocab_size - 15)
self.assertEqual(model.config.vocab_size, model_vocab_size - 15)
# Check that it actually resizes the embeddings matrix
self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
# Check that adding and removing tokens has not modified the first part of the embedding matrix.
models_equal = True
for p1, p2 in zip(cloned_embeddings, model_embed.weight):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
self.assertTrue(models_equal)
def test_tie_model_weights(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
def check_same_values(layer_1, layer_2):
equal = True
for p1, p2 in zip(layer_1.weight, layer_2.weight):
if p1.data.ne(p2.data).sum() > 0:
equal = False
return equal
for model_class in self.all_model_classes:
if not hasattr(model_class, 'tie_weights'):
continue
config.torchscript = True
model_not_tied = model_class(config)
params_not_tied = list(model_not_tied.parameters())
config_tied = copy.deepcopy(config)
config_tied.torchscript = False
model_tied = model_class(config_tied)
params_tied = list(model_tied.parameters())
# Check that the embedding layer and decoding layer are the same in size and in value
self.assertGreater(len(params_not_tied), len(params_tied))
# self.assertTrue(check_same_values(embeddings, decoding))
# # Check that after modification, they remain the same.
# embeddings.weight.data.div_(2)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
# self.assertTrue(check_same_values(embeddings, decoding))
# # Check that after modification, they remain the same.
# decoding.weight.data.div_(4)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(embeddings.weight.shape, decoding.weight.shape)
# self.assertTrue(check_same_values(embeddings, decoding))
# Check that after resize they remain tied.
model_tied.resize_token_embeddings(config.vocab_size + 10)
params_tied_2 = list(model_tied.parameters())
self.assertGreater(len(params_not_tied), len(params_tied))
self.assertEqual(len(params_tied_2), len(params_tied))
# decoding.weight.data.mul_(20)
# # Check that the embedding layer and decoding layer are the same in size and in value
# self.assertTrue(model.transformer.wte.weight.shape, model.lm_head.weight.shape)
# self.assertTrue(check_same_values(model.transformer.wte, model.lm_head))
class GPTModelTester(CommonModelTester):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_position_ids=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
n_positions=33,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
n_choices=3,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
scope=None,
config_class=None,
base_model_class=None,
lm_head_model_class=None,
double_head_model_class=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_position_ids = use_position_ids
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.n_positions = n_positions
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.n_choices = n_choices
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
self.config_class = config_class
self.base_model_class = base_model_class
self.lm_head_model_class = lm_head_model_class
self.double_head_model_class = double_head_model_class
self.all_model_classes = (base_model_class, lm_head_model_class, double_head_model_class)
def prepare_config_and_inputs(self):
total_num_tokens = self.vocab_size
input_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_num_tokens)
position_ids = None
if self.use_position_ids:
position_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_positions)
token_type_ids = None
if self.use_token_type_ids:
total_voc = self.vocab_size
token_type_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc)
mc_labels = None
lm_labels = None
mc_token_ids = None
if self.use_labels:
mc_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
lm_labels = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels)
mc_token_ids = ids_tensor([self.batch_size, self.n_choices], self.seq_length)
config = self.config_class(
vocab_size_or_config_json_file=self.vocab_size,
n_positions=self.n_positions,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
initializer_range=self.initializer_range)
return (config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids)
def create_and_check_base_model(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.base_model_class(config)
model.eval()
outputs = model(input_ids, position_ids, token_type_ids)
outputs = model(input_ids, position_ids)
outputs = model(input_ids)
hidden_state = outputs[0]
self.parent.assertListEqual(
list(hidden_state.size()),
[self.batch_size, self.n_choices, self.seq_length, self.hidden_size])
def create_and_check_lm_head(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.lm_head_model_class(config)
model.eval()
outputs = model(input_ids, position_ids, token_type_ids, lm_labels)
loss, lm_logits = outputs[:2]
total_voc = self.vocab_size
self.parent.assertListEqual(
list(lm_logits.size()),
[self.batch_size, self.n_choices, self.seq_length, total_voc])
self.parent.assertListEqual(
list(loss.size()),
[])
def create_and_check_presents(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
for model_class in self.all_model_classes:
model = model_class(config)
model.eval()
outputs = model(input_ids)
presents = outputs[-1]
self.parent.assertEqual(self.num_hidden_layers, len(presents))
self.parent.assertListEqual(
list(presents[0].size()),
[2, self.batch_size * self.n_choices, self.num_attention_heads,
self.seq_length, self.hidden_size // self.num_attention_heads])
def create_and_check_double_heads(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.double_head_model_class(config)
model.eval()
outputs = model(input_ids, mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels,
token_type_ids=token_type_ids, position_ids=position_ids)
lm_loss, mc_loss, lm_logits, mc_logits = outputs[:4]
loss = [lm_loss, mc_loss]
total_voc = self.vocab_size
self.parent.assertListEqual(
list(lm_logits.size()),
[self.batch_size, self.n_choices, self.seq_length, total_voc])
self.parent.assertListEqual(
list(mc_logits.size()),
[self.batch_size, self.n_choices])
self.parent.assertListEqual(
[list(l.size()) for l in loss],
[[], []])
def create_and_check_model_from_pretrained(self):
cache_dir = "/tmp/pytorch_transformers_test/"
for model_name in list(self.base_model_class.pretrained_model_archive_map.keys())[:1]:
model = self.base_model_class.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.parent.assertIsNotNone(model)
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids) = config_and_inputs
inputs_dict = {'input_ids': input_ids}
return config, inputs_dict
def run_common_tests(self, test_presents=False):
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_base_model(*config_and_inputs)
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_lm_head(*config_and_inputs)
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_double_heads(*config_and_inputs)
if test_presents:
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_presents(*config_and_inputs)
def run_slow_tests(self):
self.create_and_check_model_from_pretrained()
class ConfigTester(object):
def __init__(self, parent, config_class=None, **kwargs):
self.parent = parent
self.config_class = config_class
self.inputs_dict = kwargs
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, 'vocab_size'))
self.parent.assertTrue(hasattr(config, 'hidden_size'))
self.parent.assertTrue(hasattr(config, 'num_attention_heads'))
self.parent.assertTrue(hasattr(config, 'num_hidden_layers'))
def create_and_test_config_to_json_string(self):
config = self.config_class(**self.inputs_dict)
obj = json.loads(config.to_json_string())
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key], value)
def create_and_test_config_to_json_file(self):
config_first = self.config_class(**self.inputs_dict)
json_file_path = "/tmp/config.json"
config_first.to_json_file(json_file_path)
config_second = self.config_class.from_json_file(json_file_path)
os.remove(json_file_path)
self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
def run_common_tests(self):
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
def ids_tensor(shape, vocab_size, rng=None, name=None):
"""Creates a random int32 tensor of the shape within the vocab size."""
if rng is None:
rng = random.Random()
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
class ModelUtilsTest(unittest.TestCase):
def test_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = BertConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, PretrainedConfig)
model = BertModel.from_pretrained(model_name)
model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, PreTrainedModel)
for value in loading_info.values():
self.assertEqual(len(value), 0)
config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(model.config, config)
if __name__ == "__main__":
unittest.main()
pytorch_transformers/tests/modeling_gpt2_test.py
View file @ 292140b9
......@@ -16,19 +16,14 @@ from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import unittest
import json
import random
import shutil
import pytest
import torch
from pytorch_transformers import (GPT2Config, GPT2Model,
GPT2LMHeadModel, GPT2DoubleHeadsModel)
from .modeling_tests_commons import (create_and_check_commons, ConfigTester, GPTModelTester)
from .modeling_common_test import CommonTestCases, ConfigTester
class GPT2ModelTest(unittest.TestCase):
......@@ -37,14 +32,14 @@ class GPT2ModelTest(unittest.TestCase):
config_tester.run_common_tests()
def test_model(self):
model_tester = GPTModelTester(self, config_class=GPT2Config, base_model_class=GPT2Model,
model_tester = CommonTestCases.GPTModelTester(self, config_class=GPT2Config, base_model_class=GPT2Model,
lm_head_model_class=GPT2LMHeadModel,
double_head_model_class=GPT2DoubleHeadsModel)
model_tester.run_common_tests(test_presents=True)
@pytest.mark.slow
def test_pretrained(self):
model_tester = GPTModelTester(self, config_class=GPT2Config, base_model_class=GPT2Model,
model_tester = CommonTestCases.GPTModelTester(self, config_class=GPT2Config, base_model_class=GPT2Model,
lm_head_model_class=GPT2LMHeadModel,
double_head_model_class=GPT2DoubleHeadsModel)
model_tester.run_slow_tests()
......
pytorch_transformers/tests/modeling_openai_test.py
View file @ 292140b9
......@@ -19,12 +19,11 @@ from __future__ import print_function
import unittest
import pytest
import torch
from pytorch_transformers import (OpenAIGPTConfig, OpenAIGPTModel,
OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel)
from .modeling_tests_commons import (create_and_check_commons, ConfigTester, GPTModelTester)
from .modeling_common_test import CommonTestCases, ConfigTester
class OpenAIModelTest(unittest.TestCase):
......@@ -33,14 +32,14 @@ class OpenAIModelTest(unittest.TestCase):
config_tester.run_common_tests()
def test_model(self):
model_tester = GPTModelTester(self, config_class=OpenAIGPTConfig, base_model_class=OpenAIGPTModel,
model_tester = CommonTestCases.GPTModelTester(self, config_class=OpenAIGPTConfig, base_model_class=OpenAIGPTModel,
lm_head_model_class=OpenAIGPTLMHeadModel,
double_head_model_class=OpenAIGPTDoubleHeadsModel)
model_tester.run_common_tests(test_presents=False)
@pytest.mark.slow
def test_pretrained(self):
model_tester = GPTModelTester(self, config_class=OpenAIGPTConfig, base_model_class=OpenAIGPTModel,
model_tester = CommonTestCases.GPTModelTester(self, config_class=OpenAIGPTConfig, base_model_class=OpenAIGPTModel,
lm_head_model_class=OpenAIGPTLMHeadModel,
double_head_model_class=OpenAIGPTDoubleHeadsModel)
model_tester.run_slow_tests()
......
pytorch_transformers/tests/modeling_tests_commons.py deleted 100644 → 0
View file @ 3821ecbf
# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import copy
import os
import shutil
import json
import random
import torch
def _config_zero_init(config):
configs_no_init = copy.deepcopy(config)
for key in configs_no_init.__dict__.keys():
if '_range' in key or '_std' in key:
setattr(configs_no_init, key, 0.0)
return configs_no_init
def _create_and_check_torchscript_output_attentions(tester, model_classes, config, inputs_dict):
config.output_attentions = True
_create_and_check_torchscript(tester, model_classes, config, inputs_dict)
def _create_and_check_torchscript_output_hidden_state(tester, model_classes, config, inputs_dict):
config.output_hidden_states = True
_create_and_check_torchscript(tester, model_classes, config, inputs_dict)
def _create_and_check_torchscript(tester, model_classes, config, inputs_dict):
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
configs_no_init.torchscript = True
for model_class in model_classes:
model = model_class(config=configs_no_init)
model.eval()
inputs = inputs_dict['input_ids'] # Let's keep only input_ids
try:
torch.jit.trace(model, inputs)
except RuntimeError:
tester.parent.fail("Couldn't trace module.")
try:
traced_gpt2 = torch.jit.trace(model, inputs)
torch.jit.save(traced_gpt2, "traced_model.pt")
except RuntimeError:
tester.parent.fail("Couldn't save module.")
try:
loaded_model = torch.jit.load("traced_model.pt")
os.remove("traced_model.pt")
except ValueError:
tester.parent.fail("Couldn't load module.")
model.eval()
loaded_model.eval()
model_params = model.parameters()
loaded_model_params = loaded_model.parameters()
models_equal = True
for p1, p2 in zip(model_params, loaded_model_params):
if p1.data.ne(p2.data).sum() > 0:
models_equal = False
tester.parent.assertTrue(models_equal)
def _create_and_check_initialization(tester, model_classes, config, inputs_dict):
configs_no_init = _config_zero_init(config)
for model_class in model_classes:
model = model_class(config=configs_no_init)
for name, param in model.named_parameters():
if param.requires_grad:
tester.parent.assertIn(param.data.mean().item(), [0.0, 1.0],
msg="Parameter {} of model {} seems not properly initialized".format(name, model_class))
def _create_and_check_for_headmasking(tester, model_classes, config, inputs_dict):
configs_no_init = _config_zero_init(config) # To be sure we have no Nan
for model_class in model_classes:
config.output_attentions = True
config.output_hidden_states = True
model = model_class(config=configs_no_init)
model.eval()
# Prepare head_mask
# Set require_grad after having prepared the tensor to avoid error (leaf variable has been moved into the graph interior)
head_mask = torch.ones(tester.num_hidden_layers, tester.num_attention_heads)
head_mask[0, 0] = 0
head_mask[-1, :-1] = 0
head_mask.requires_grad_(requires_grad=True)
inputs = inputs_dict.copy()
inputs['head_mask'] = head_mask
outputs = model(**inputs)
# Test that we can get a gradient back for importance score computation
output = sum(t.sum() for t in outputs[0])
output = output.sum()
output.backward()
multihead_outputs = head_mask.grad
attentions = outputs[-1]
hidden_states = outputs[-2]
# Remove Nan
tester.parent.assertIsNotNone(multihead_outputs)
tester.parent.assertEqual(len(multihead_outputs), tester.num_hidden_layers)
tester.parent.assertAlmostEqual(
attentions[0][..., 0, :, :].flatten().sum().item(), 0.0)
tester.parent.assertNotEqual(
attentions[0][..., -1, :, :].flatten().sum().item(), 0.0)
tester.parent.assertNotEqual(
attentions[1][..., 0, :, :].flatten().sum().item(), 0.0)
tester.parent.assertAlmostEqual(
attentions[-1][..., -2, :, :].flatten().sum().item(), 0.0)
tester.parent.assertNotEqual(
attentions[-1][..., -1, :, :].flatten().sum().item(), 0.0)
def _create_and_check_for_head_pruning(tester, model_classes, config, inputs_dict):
for model_class in model_classes:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config=config)
model.eval()
heads_to_prune = {0: list(range(1, tester.num_attention_heads)),
-1: [0]}
model.prune_heads(heads_to_prune)
outputs = model(**inputs_dict)
attentions = outputs[-1]
tester.parent.assertEqual(
attentions[0].shape[-3], 1)
tester.parent.assertEqual(
attentions[1].shape[-3], tester.num_attention_heads)
tester.parent.assertEqual(
attentions[-1].shape[-3], tester.num_attention_heads - 1)
def _create_and_check_for_attentions(tester, model_classes, config, inputs_dict):
for model_class in model_classes:
config.output_attentions = True
config.output_hidden_states = False
model = model_class(config)
model.eval()
outputs = model(**inputs_dict)
attentions = outputs[-1]
tester.parent.assertEqual(model.config.output_attentions, True)
tester.parent.assertEqual(model.config.output_hidden_states, False)
tester.parent.assertEqual(len(attentions), tester.num_hidden_layers)
tester.parent.assertListEqual(
list(attentions[0].shape[-3:]),
[tester.num_attention_heads,
tester.seq_length,
tester.key_len if hasattr(tester, 'key_len') else tester.seq_length])
out_len = len(outputs)
# Check attention is always last and order is fine
config.output_attentions = True
config.output_hidden_states = True
model = model_class(config)
model.eval()
outputs = model(**inputs_dict)
tester.parent.assertEqual(out_len+1, len(outputs))
tester.parent.assertEqual(model.config.output_attentions, True)
tester.parent.assertEqual(model.config.output_hidden_states, True)
attentions = outputs[-1]
tester.parent.assertEqual(len(attentions), tester.num_hidden_layers)
tester.parent.assertListEqual(
list(attentions[0].shape[-3:]),
[tester.num_attention_heads,
tester.seq_length,
tester.key_len if hasattr(tester, 'key_len') else tester.seq_length])
def _create_and_check_for_hidden_states(tester, model_classes, config, inputs_dict):
for model_class in model_classes:
config.output_hidden_states = True
config.output_attentions = False
model = model_class(config)
model.eval()
outputs = model(**inputs_dict)
hidden_states = outputs[-1]
tester.parent.assertEqual(model.config.output_attentions, False)
tester.parent.assertEqual(model.config.output_hidden_states, True)
tester.parent.assertEqual(len(hidden_states), tester.num_hidden_layers + 1)
tester.parent.assertListEqual(
list(hidden_states[0].shape[-2:]),
[tester.seq_length, tester.hidden_size])
def create_and_check_commons(tester, config, inputs_dict, test_pruning=True, test_torchscript=True):
_create_and_check_initialization(tester, tester.all_model_classes, config, inputs_dict)
_create_and_check_for_attentions(tester, tester.all_model_classes, config, inputs_dict)
_create_and_check_for_headmasking(tester, tester.all_model_classes, config, inputs_dict)
_create_and_check_for_hidden_states(tester, tester.all_model_classes, config, inputs_dict)
if test_torchscript:
_create_and_check_torchscript(tester, tester.all_model_classes, config, inputs_dict)
_create_and_check_torchscript_output_attentions(tester, tester.all_model_classes, config, inputs_dict)
_create_and_check_torchscript_output_hidden_state(tester, tester.all_model_classes, config, inputs_dict)
if test_pruning:
_create_and_check_for_head_pruning(tester, tester.all_model_classes, config, inputs_dict)
def ids_tensor(shape, vocab_size, rng=None, name=None):
"""Creates a random int32 tensor of the shape within the vocab size."""
if rng is None:
rng = random.Random()
total_dims = 1
for dim in shape:
total_dims *= dim
values = []
for _ in range(total_dims):
values.append(rng.randint(0, vocab_size - 1))
return torch.tensor(data=values, dtype=torch.long).view(shape).contiguous()
class ConfigTester(object):
def __init__(self, parent, config_class=None, **kwargs):
self.parent = parent
self.config_class = config_class
self.inputs_dict = kwargs
def create_and_test_config_common_properties(self):
config = self.config_class(**self.inputs_dict)
self.parent.assertTrue(hasattr(config, 'hidden_size'))
self.parent.assertTrue(hasattr(config, 'num_attention_heads'))
self.parent.assertTrue(hasattr(config, 'num_hidden_layers'))
def create_and_test_config_to_json_string(self):
config = self.config_class(**self.inputs_dict)
obj = json.loads(config.to_json_string())
for key, value in self.inputs_dict.items():
self.parent.assertEqual(obj[key], value)
def create_and_test_config_to_json_file(self):
config_first = self.config_class(**self.inputs_dict)
json_file_path = "/tmp/config.json"
config_first.to_json_file(json_file_path)
config_second = self.config_class.from_json_file(json_file_path)
os.remove(json_file_path)
self.parent.assertEqual(config_second.to_dict(), config_first.to_dict())
def run_common_tests(self):
self.create_and_test_config_common_properties()
self.create_and_test_config_to_json_string()
self.create_and_test_config_to_json_file()
class GPTModelTester(object):
def __init__(self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_position_ids=True,
use_token_type_ids=True,
use_labels=True,
vocab_size=99,
n_special=1,
n_positions=33,
hidden_size=32,
num_hidden_layers=5,
num_attention_heads=4,
n_choices=3,
type_sequence_label_size=2,
initializer_range=0.02,
num_labels=3,
scope=None,
config_class=None,
base_model_class=None,
lm_head_model_class=None,
double_head_model_class=None,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_position_ids = use_position_ids
self.use_token_type_ids = use_token_type_ids
self.use_labels = use_labels
self.vocab_size = vocab_size
self.n_special = n_special
self.n_positions = n_positions
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.n_choices = n_choices
self.type_sequence_label_size = type_sequence_label_size
self.initializer_range = initializer_range
self.num_labels = num_labels
self.scope = scope
self.config_class = config_class
self.base_model_class = base_model_class
self.lm_head_model_class = lm_head_model_class
self.double_head_model_class = double_head_model_class
self.all_model_classes = (base_model_class, lm_head_model_class, double_head_model_class)
def prepare_config_and_inputs(self):
total_num_tokens = self.vocab_size + self.n_special
input_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_num_tokens)
position_ids = None
if self.use_position_ids:
position_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.n_positions)
token_type_ids = None
if self.use_token_type_ids:
total_voc = self.vocab_size
token_type_ids = ids_tensor([self.batch_size, self.n_choices, self.seq_length], total_voc)
mc_labels = None
lm_labels = None
mc_token_ids = None
if self.use_labels:
mc_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
lm_labels = ids_tensor([self.batch_size, self.n_choices, self.seq_length], self.num_labels)
mc_token_ids = ids_tensor([self.batch_size, self.n_choices], self.seq_length)
config = self.config_class(
vocab_size_or_config_json_file=self.vocab_size,
n_special=self.n_special,
n_positions=self.n_positions,
n_embd=self.hidden_size,
n_layer=self.num_hidden_layers,
n_head=self.num_attention_heads,
initializer_range=self.initializer_range)
return (config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids)
def create_and_check_base_model(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.base_model_class(config)
model.eval()
outputs = model(input_ids, position_ids, token_type_ids)
outputs = model(input_ids, position_ids)
outputs = model(input_ids)
hidden_state = outputs[0]
self.parent.assertListEqual(
list(hidden_state.size()),
[self.batch_size, self.n_choices, self.seq_length, self.hidden_size])
def create_and_check_lm_head(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.lm_head_model_class(config)
model.eval()
outputs = model(input_ids, position_ids, token_type_ids, lm_labels)
loss, lm_logits = outputs[:2]
total_voc = self.n_special + self.vocab_size
self.parent.assertListEqual(
list(lm_logits.size()),
[self.batch_size, self.n_choices, self.seq_length, total_voc])
self.parent.assertListEqual(
list(loss.size()),
[])
def create_and_check_presents(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
for model_class in self.all_model_classes:
model = model_class(config)
model.eval()
outputs = model(input_ids)
presents = outputs[-1]
self.parent.assertEqual(self.num_hidden_layers, len(presents))
self.parent.assertListEqual(
list(presents[0].size()),
[2, self.batch_size * self.n_choices, self.num_attention_heads,
self.seq_length, self.hidden_size // self.num_attention_heads])
def create_and_check_double_heads(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
model = self.double_head_model_class(config)
model.eval()
outputs = model(input_ids, mc_token_ids, lm_labels=lm_labels, mc_labels=mc_labels,
token_type_ids=token_type_ids, position_ids=position_ids)
lm_loss, mc_loss, lm_logits, mc_logits = outputs[:4]
loss = [lm_loss, mc_loss]
total_voc = self.n_special + self.vocab_size
self.parent.assertListEqual(
list(lm_logits.size()),
[self.batch_size, self.n_choices, self.seq_length, total_voc])
self.parent.assertListEqual(
list(mc_logits.size()),
[self.batch_size, self.n_choices])
self.parent.assertListEqual(
[list(l.size()) for l in loss],
[[], []])
def create_and_check_model_from_pretrained(self):
cache_dir = "/tmp/pytorch_transformers_test/"
for model_name in list(self.base_model_class.pretrained_model_archive_map.keys())[:1]:
model = self.base_model_class.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.parent.assertIsNotNone(model)
def create_and_check_commons(self, config, input_ids, token_type_ids, position_ids,
mc_labels, lm_labels, mc_token_ids):
inputs_dict = {'input_ids': input_ids}
create_and_check_commons(self, config, inputs_dict)
def run_common_tests(self, test_presents=False):
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_base_model(*config_and_inputs)
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_lm_head(*config_and_inputs)
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_double_heads(*config_and_inputs)
if test_presents:
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_presents(*config_and_inputs)
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_commons(*config_and_inputs)
def run_slow_tests(self):
config_and_inputs = self.prepare_config_and_inputs()
self.create_and_check_model_from_pretrained(*config_and_inputs)
pytorch_transformers/tests/modeling_transfo_xl_test.py
View file @ 292140b9
......@@ -28,9 +28,15 @@ import torch
from pytorch_transformers import (TransfoXLConfig, TransfoXLModel, TransfoXLLMHeadModel)
from pytorch_transformers.modeling_transfo_xl import TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_MAP
from .modeling_tests_commons import ConfigTester, create_and_check_commons, ids_tensor
from .modeling_common_test import ConfigTester, CommonTestCases, ids_tensor
class TransfoXLModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (TransfoXLModel, TransfoXLLMHeadModel)
test_pruning = False
test_torchscript = False
test_resize_embeddings = False
class TransfoXLModelTest(unittest.TestCase):
class TransfoXLModelTester(object):
def __init__(self,
......@@ -52,7 +58,6 @@ class TransfoXLModelTest(unittest.TestCase):
num_hidden_layers=5,
scope=None,
seed=1,
all_model_classes=(TransfoXLModel, TransfoXLLMHeadModel),
):
self.parent = parent
self.batch_size = batch_size
......@@ -73,7 +78,6 @@ class TransfoXLModelTest(unittest.TestCase):
self.num_hidden_layers = num_hidden_layers
self.scope = scope
self.seed = seed
self.all_model_classes = all_model_classes
def prepare_config_and_inputs(self):
input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
......@@ -171,16 +175,31 @@ class TransfoXLModelTest(unittest.TestCase):
list(list(mem.size()) for mem in result["mems_2"]),
[[self.mem_len, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_transfo_xl_commons(self, config, input_ids_1, input_ids_2, lm_labels):
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids_1, input_ids_2, lm_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids_1}
create_and_check_commons(self, config, inputs_dict, test_pruning=False, test_torchscript=False)
return config, inputs_dict
def test_default(self):
self.run_tester(TransfoXLModelTest.TransfoXLModelTester(self))
def setUp(self):
self.model_tester = TransfoXLModelTest.TransfoXLModelTester(self)
self.config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37)
def test_config(self):
config_tester = ConfigTester(self, config_class=TransfoXLConfig, d_embed=37)
config_tester.run_common_tests()
self.config_tester.run_common_tests()
def test_transfo_xl_model(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
output_result = self.model_tester.create_transfo_xl_model(*config_and_inputs)
self.model_tester.check_transfo_xl_model_output(output_result)
def test_transfo_xl_lm_head(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
output_result = self.model_tester.create_transfo_xl_lm_head(*config_and_inputs)
self.model_tester.check_transfo_xl_lm_head_output(output_result)
@pytest.mark.slow
def test_model_from_pretrained(self):
......@@ -190,23 +209,6 @@ class TransfoXLModelTest(unittest.TestCase):
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
def run_tester(self, tester):
config_and_inputs = tester.prepare_config_and_inputs()
tester.set_seed()
config_and_inputs = tester.prepare_config_and_inputs()
output_result = tester.create_transfo_xl_model(*config_and_inputs)
tester.check_transfo_xl_model_output(output_result)
tester.set_seed()
config_and_inputs = tester.prepare_config_and_inputs()
output_result = tester.create_transfo_xl_lm_head(*config_and_inputs)
tester.check_transfo_xl_lm_head_output(output_result)
tester.set_seed()
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_transfo_xl_commons(*config_and_inputs)
if __name__ == "__main__":
unittest.main()
pytorch_transformers/tests/modeling_utils_test.py deleted 100644 → 0
View file @ 3821ecbf
# coding=utf-8
# Copyright 2018 HuggingFace Inc..
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unittest
import logging
from pytorch_transformers import PretrainedConfig, PreTrainedModel
from pytorch_transformers.modeling_bert import BertModel, BertConfig, BERT_PRETRAINED_MODEL_ARCHIVE_MAP
class ModelUtilsTest(unittest.TestCase):
def test_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = BertConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, PretrainedConfig)
model = BertModel.from_pretrained(model_name)
model, loading_info = BertModel.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, PreTrainedModel)
for value in loading_info.values():
self.assertEqual(len(value), 0)
config = BertConfig.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
model = BertModel.from_pretrained(model_name, output_attentions=True, output_hidden_states=True)
self.assertEqual(model.config.output_attentions, True)
self.assertEqual(model.config.output_hidden_states, True)
self.assertEqual(model.config, config)
if __name__ == "__main__":
unittest.main()
pytorch_transformers/tests/modeling_xlm_test.py
View file @ 292140b9
......@@ -23,10 +23,15 @@ import pytest
from pytorch_transformers import (XLMConfig, XLMModel, XLMWithLMHeadModel, XLMForQuestionAnswering, XLMForSequenceClassification)
from pytorch_transformers.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_MAP
from .modeling_tests_commons import (create_and_check_commons, ConfigTester, ids_tensor)
from .modeling_common_test import (CommonTestCases, ConfigTester, ids_tensor)
class XLMModelTest(unittest.TestCase):
class XLMModelTest(CommonTestCases.CommonModelTester):
all_model_classes = (XLMModel, XLMWithLMHeadModel,
XLMForQuestionAnswering, XLMForSequenceClassification)
# , XLMForSequenceClassification, XLMForTokenClassification),
class XLMModelTester(object):
def __init__(self,
......@@ -58,8 +63,6 @@ class XLMModelTest(unittest.TestCase):
summary_type="last",
use_proj=True,
scope=None,
all_model_classes = (XLMModel, XLMWithLMHeadModel,
XLMForQuestionAnswering, XLMForSequenceClassification), # , XLMForSequenceClassification, XLMForTokenClassification),
):
self.parent = parent
self.batch_size = batch_size
......@@ -90,7 +93,6 @@ class XLMModelTest(unittest.TestCase):
self.num_labels = num_labels
self.num_choices = num_choices
self.scope = scope
self.all_model_classes = all_model_classes
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
......@@ -237,28 +239,23 @@ class XLMModelTest(unittest.TestCase):
[self.batch_size, self.type_sequence_label_size])
def create_and_check_xlm_commons(self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, input_mask):
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids, token_type_ids, input_lengths,
sequence_labels, token_labels, is_impossible_labels, input_mask) = config_and_inputs
inputs_dict = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'lengths': input_lengths}
create_and_check_commons(self, config, inputs_dict)
return config, inputs_dict
def test_default(self):
self.run_tester(XLMModelTest.XLMModelTester(self))
def setUp(self):
self.model_tester = XLMModelTest.XLMModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=37)
def test_config(self):
config_tester = ConfigTester(self, config_class=XLMConfig, emb_dim=37)
config_tester.run_common_tests()
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/pytorch_transformers_test/"
for model_name in list(XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = XLMModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
self.config_tester.run_common_tests()
def run_tester(self, tester):
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_xlm_model(*config_and_inputs)
def test_xlm_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlm_model(*config_and_inputs)
# config_and_inputs = tester.prepare_config_and_inputs()
# tester.create_and_check_xlm_for_masked_lm(*config_and_inputs)
......@@ -275,8 +272,14 @@ class XLMModelTest(unittest.TestCase):
# config_and_inputs = tester.prepare_config_and_inputs()
# tester.create_and_check_xlm_for_token_classification(*config_and_inputs)
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_xlm_commons(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
cache_dir = "/tmp/pytorch_transformers_test/"
for model_name in list(XLM_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
model = XLMModel.from_pretrained(model_name, cache_dir=cache_dir)
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
if __name__ == "__main__":
unittest.main()
pytorch_transformers/tests/modeling_xlnet_test.py
View file @ 292140b9
......@@ -28,9 +28,14 @@ import torch
from pytorch_transformers import (XLNetConfig, XLNetModel, XLNetLMHeadModel, XLNetForSequenceClassification, XLNetForQuestionAnswering)
from pytorch_transformers.modeling_xlnet import XLNET_PRETRAINED_MODEL_ARCHIVE_MAP
from .modeling_tests_commons import ConfigTester, create_and_check_commons, ids_tensor
from .modeling_common_test import ConfigTester, CommonTestCases, ids_tensor
class XLNetModelTest(CommonTestCases.CommonModelTester):
all_model_classes=(XLNetModel, XLNetLMHeadModel,
XLNetForSequenceClassification, XLNetForQuestionAnswering)
test_pruning = False
class XLNetModelTest(unittest.TestCase):
class XLNetModelTester(object):
def __init__(self,
......@@ -56,8 +61,6 @@ class XLNetModelTest(unittest.TestCase):
initializer_range=0.05,
seed=1,
type_vocab_size=2,
all_model_classes=(XLNetModel, XLNetLMHeadModel,
XLNetForSequenceClassification, XLNetForQuestionAnswering),
):
self.parent = parent
self.batch_size = batch_size
......@@ -82,7 +85,6 @@ class XLNetModelTest(unittest.TestCase):
self.seed = seed
self.type_vocab_size = type_vocab_size
self.type_sequence_label_size = type_sequence_label_size
self.all_model_classes = all_model_classes
def prepare_config_and_inputs(self):
input_ids_1 = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
......@@ -264,17 +266,41 @@ class XLNetModelTest(unittest.TestCase):
list(list(mem.size()) for mem in result["mems_1"]),
[[self.seq_length, self.batch_size, self.hidden_size]] * self.num_hidden_layers)
def create_and_check_xlnet_commons(self, config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, inp_q, segment_ids, lm_labels, sequence_labels, is_impossible_labels):
def prepare_config_and_inputs_for_common(self):
config_and_inputs = self.prepare_config_and_inputs()
(config, input_ids_1, input_ids_2, input_ids_q, perm_mask, input_mask,
target_mapping, inp_q, segment_ids, lm_labels,
sequence_labels, is_impossible_labels) = config_and_inputs
inputs_dict = {'input_ids': input_ids_1}
create_and_check_commons(self, config, inputs_dict, test_pruning=False)
return config, inputs_dict
def test_default(self):
self.run_tester(XLNetModelTest.XLNetModelTester(self))
def setUp(self):
self.model_tester = XLNetModelTest.XLNetModelTester(self)
self.config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37)
def test_config(self):
config_tester = ConfigTester(self, config_class=XLNetConfig, d_inner=37)
config_tester.run_common_tests()
self.config_tester.run_common_tests()
def test_xlnet_base_model(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_base_model(*config_and_inputs)
def test_xlnet_lm_head(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_lm_head(*config_and_inputs)
def test_xlnet_sequence_classif(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)
def test_xlnet_qa(self):
self.model_tester.set_seed()
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_xlnet_qa(*config_and_inputs)
@pytest.mark.slow
def test_model_from_pretrained(self):
......@@ -284,27 +310,6 @@ class XLNetModelTest(unittest.TestCase):
shutil.rmtree(cache_dir)
self.assertIsNotNone(model)
def run_tester(self, tester):
tester.set_seed()
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_xlnet_base_model(*config_and_inputs)
tester.set_seed()
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_xlnet_lm_head(*config_and_inputs)
tester.set_seed()
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_xlnet_sequence_classif(*config_and_inputs)
tester.set_seed()
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_xlnet_qa(*config_and_inputs)
tester.set_seed()
config_and_inputs = tester.prepare_config_and_inputs()
tester.create_and_check_xlnet_commons(*config_and_inputs)
if __name__ == "__main__":
unittest.main()
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